Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHR) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a RWD collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of metastatic renal cell carcinoma (RCC) patients receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared to manual review for validation. 100 patients receiving 175 treatments were included using CDC, which corresponded to 99% with manual review. Calculated median overall survival was 21.7 months (95% CI 18.7 – 24.8) versus 21.7 months (95% CI 18.6 – 24.8) and progression-free survival 8.9 months (95% CI 5.4 – 12.4) versus 7.6 months (95% CI 5.7 – 9.4) for CDC versus MR respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5 – 90.4) and adverse drug events (53.3 – 74.5), with most diverse scores on international metastatic RCC database criteria (51.4 – 100). Mean data collection time was 12 minutes (CDC) versus 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs since the correct patient population can be identified as well as relevant outcome data such as overall survival and progression-free survival.
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